Method and system for capacity analysis for On The Move adhoc wireless packet-switched networks

ABSTRACT

A system and method for capacity analysis in communication networks, particularly for on the move ad hoc wireless packet-switched networks, as well as wide variety of other multimedia networks. The invention seeks to use the same two attributes per link (link capacity and link utilization) as known circuit-switched based analysis tools while incorporating useful aspects of various statistical analyses, such as a Queuing Theory based analysis, among others. In one embodiment, the invention introduces four tests to be implemented per each link, with results of these four tests being used to color code link congestion states to generate the reports for a planner. These four tests may generate an improved analysis of the network utilizing the same number of variables used in simple conventional circuit switched based analysis.

PRIORITY

This application claims priority to U.S. Provisional Patent ApplicationNo. 60/550,374, filed Mar. 5, 2004 and entitled “A METHOD AND SYSTEM FORCAPACITY ANALYSIS FOR ON THE MOVE ADHOC WIRELESS PACKET-SWITCHEDNETWORKS. That application is hereby incorporated herein by reference inits entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates generally to capacity analysis and planning incommunication networks. More particularly, the invention relates tocapacity analysis based on such factors as link effective bandwidth andlink utilization. Even more particularly, the invention may be appliedto On The Move (OTM) ad-hoc wireless packet-switched networks and/ormultimedia wireless ad-hoc OTM networks (e.g. future tactical networks).

2. Related Art

By way of background, OTM ad-hoc packet-switched wireless network (e.g.future tactical network) links carry different types of multiplexedpackets (voice, video and data). This raises the desire to have capacityanalysis and planning tools that supplant circuit-switched basedplanning tools. Packet switched capacity analysis tools may consider thestatistically multiplexed heterogeneous traffic using the network and atthe same time may perform simple computations to allow a planner tosimulate long runs (scenarios) for very large networks (thousands ofnodes) considering all the stages and formation of the OTM network in avery short time.

Typical circuit-switched based capacity analysis tools utilize twoattributes per link (link capacity and link utilization). Link capacityis the actual trunk size, while link utilization is the reservedbandwidth over the link. Certain known systems rely only on acharacteristic denoted “headroom,” i.e., a difference between maximumcapacity and current traffic. As the link utilization approaches thelink capacity (with allocating more calls/sessions over the link),circuit switch based tools indicate link congestion level and reportsare generated to the planner. For example, on a Graphical User Interface(GUI) or other such tool accessible by the planner, color or anotherdesignator may be used to indicate the congestion or other factors ofinterest to the planner. Such systems have been found to be insufficientin certain circumstances.

With packet-switched networks, the situation differs due to the natureof statistically multiplexed heterogeneous traffic. On one hand,standard Queuing Theory analysis produces complex mathematical formulasthat use knowledge of many factors like packet size, traffic shape,router scheduling, etc. Many of these parameters may not be available tothe planner. On the other hand, following circuit switched basedanalysis may tend to produce inaccurate results. What is needed is asystem for providing more reliable results. In one embodiment, it isdesired that these results be achieved despite maintaining a certainsimplicity of known systems in that only traffic and capacity bereceived as inputs.

SUMMARY OF THE INVENTION

A system and method of the invention seek to analyze planned networks ina dynamic, robust and accurate manner. While the invention may be usefulfor the art of capacity analysis and planning for wireless ad-hoc OTMnetworks, and will at times be described with specific referencethereto, it should be noted that the invention is also applicable toother fields and applications. For example, the invention may be used ina variety of environments that analyze wired or wireless links,including cellular, satellite, etc., and including a wide variety ofother multimedia networks. The results of this analysis can also be usedfor applications including, but not limited to, Call Admission Control(CAC), topology management and gauging QoS (Quality of Service). Furtherdescription relating to QoS within a packet switched network can befound in co-pending U.S. patent application Ser. No. 10/813,603, filedMar. 31, 2004 and entitled Call Admission Control/Session ManagementBased On N Source To Destination Severity Levels For IP Networks. Thatapplication, which describes using a combined call admissioncontrol/session management (CAC/SM) algorithm, is hereby incorporatedherein by reference in its entirety.

The present invention introduces a new method and system for capacityanalysis, such as for OTM ad-hoc wireless networks, and seeks to use thesame two attributes per link (link capacity and link utilization) asknown circuit-switched based analysis tools while incorporating usefulaspects of various statistical analyses, such as a Queuing Theory basedanalysis, among others.

In one embodiment, the invention introduces four tests to be implementedper each link, with results of these four tests being used to color codelink congestion states to generate the necessary reports for theplanner. These four tests may generate an improved analysis of thenetwork utilizing the same number of variables used in simpleconventional circuit switched based analysis. The simplicity of theanalysis may make possible a tool that allows the planner to do multiplesimulation and/or to answer “what if” questions by running fullscenarios in less time. Exemplary values and thresholds for such tests,as well as observed examples and results, are provided below.

Further details and exemplary implementations are provided below in adetailed description of embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional features and advantages of the present invention will becomemore fully apparent from a review of the following detailed descriptionof embodiments of the invention, along with the accompanying drawings,in which:

FIG. 1 is a flow diagram showing the sequence of events used by thecapacity analysis tool indicating where the algorithm is utilized inaccordance with an embodiment of the invention;

FIG. 2 shows a plot of the expected value of link delay versus percentlink utilization for three sizes of links in accordance with anembodiment of the present invention; and

FIG. 3 shows a plot of percent utilization versus link capacity, with anexpected value of delay upper-bounded by T_(U)=2 microseconds per bitand lower-bounded by T_(L)=0.2 microseconds per bit in accordance withan embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention offers a capacity analysis tool for a planner thatmay perform sampling of network traffic at snapshot times (each snapshotmay have a different formation of the wireless ad-hoc OTM network). Toperform this sampling, the tool may use knowledge of the aggregatetraffic load (from any or all classes of services) at the snapshot timebetween each active source and destination node in the network. Suchcharacteristics are commonly measured in bits-per-second (bps), althoughother methodologies may be used as well. In one embodiment, traffic loadis extracted from information known about the relevant system. Forexample, Information Exchange Requirements (IERs) might be utilized.IERs provide a description and/or outline requirements for the exchangeof information. Often, these requirements are given as a set ofcharacteristics, such as source and destination, size, speed andcontent, and may also include aspects of security or others. IERs may ormay not be dependent on a communication used. Knowing the path between asource and destination (given the network topology at the snapshottime), the tool is able to accumulate the traffic over each link in thepath. By considering all the communication activities of the IERs at thesnapshot time, the tool can determine the traffic in bps over each linkin the planned network at each snapshot time.

Many implementations for the present invention are contemplated. In oneembodiment, the invention is applied in a military context, such as by aNetOps (Network Operations) planner. In advance of an anticipatedmilitary operation, such a planner may reference a color-coded networkchart or display (as noted above), and run scenarios or simulationsusing varying factors. For example, a planner may vary link/networkcapacities, topologies, etc., to determine an optimal arrangement for agiven operation. The invention may be used to generate an indication oflink and/or network health, such as a health score. Examples of this areprovided below. In this way or others, the invention may also be usefulin generating a network management policy.

The invention is able to use the knowledge of this bps and the size ofeach link in the planned network. Note that these two parameters (bpsand link size) are the same parameters commonly used to plan a circuitswitched network. Also note that for OTM ad-hoc networks the size of aradio link may vary based on the location of the two ends of the link,terrain, and characteristics of the radio links. The term effectivebandwidth is used herein to refer to the effective link size at a givensnapshot time.

FIG. 1 depicts a flow diagram 100 showing the sequence of events used bythe capacity analysis tool or engine 180 indicating where and/or how thealgorithm is utilized in accordance with an embodiment of the presentinvention. For purposes of illustration, it is assumed that thisembodiment relates to a planned battlefield theatre. Item 110 in FIG. 1represents IERs for the planned battlefield theatre. The IERs in thiscase are the expected pattern of information flow between the networknodes (e.g., operational needs). The IERs are fed to a utility program120 that produces high fidelity traffic scripts 130. These high fidelitytraffic scripts 130 give detailed information about each call (starttime, length, rate, precedence, etc.). Regarding precedence, certaintraffic may, for example, be considered low-precedence, high-precedenceor otherwise due to such factors as a status (e.g., rank), location orother characteristic of a transmitter or sender, an urgency of themessage, etc. The high fidelity traffic scripts are then passed througha utility 140 that samples the traffic during the period of the plannedmaneuver time to generate snapshots of traffic 150. That is, it producesthe data rate going between a source and a destination at a givensampling time, among other parameters. A topology agent 170 providesinformation about a topology of a network to a traffic lay-down utility160. The traffic lay-down utility 160 provides this information, as wellas information provided in the snapshots traffic 150, to a plannednetwork information repository 190. This repository 190, as noted below,may include such information as a description of nodes, links and/ortraffic of a network. In one embodiment, this information is maintaineddynamically, changing in real-time with changes in traffic and networkfeatures.

For illustrative purposes, a behavior of the analysis tool 180 in onesuch an embodiment of the invention will now be summarized in thefollowing algorithm format:

-   -   1: A traffic lay-down utility 160 opens each traffic demand        (which is defined as the transfer data rate between a source and        destination) at the given sampling time.    -   2: For the given topology at the sampling time (which is        obtained from a topology agent 170) find the path traffic will        take (using whichever algorithm(s) or technique(s) a relevant        router uses, e.g., shortest path finder SPF).    -   3: Lay down (e.g., receive, apply) the traffic demand over each        link in the found path. For each link accumulate the traffic        from all traffic demands that will use the link. Update the        planned network information repository with the accumulated        traffic demand for each link.    -   4: Go to 1 if there are more traffic demand records at the given        sampling time.    -   5: If we are done with all the records at the given sampling        time, then the planned network information repository is ready        for the analysis engine 180.    -   6: The analysis engine 180 does the following:        -   a—Finds the accumulated traffic over each link.        -   b—Finds the link capacity (maximum bps that can go over the            link).        -   c—Apply tests 1, 2, 3, and 4 and update a planned network            information repository 190 with test results. Such a            repository might include link information including, but not            limited to, link size, utilization, health, etc.        -   d—Find each link's overall health and update planned network            information repository with link health results.    -    The link overall health may be reported to the topology agent        170, which may use it to modify the topology for next snapshot        time.    -   7: At the next sampling time go to 1.    -   8: If done with all sampling time (i.e., scenario is complete),        analyze the collected information to generate a report about the        planned scenario given the test results from 6-c and 6-d.

Any or all of the items illustrated in FIG. 1 may be embodied in avariety of ways. In one embodiment, IERs 100, hi-fidelity traffic 130and snapshots 150 represent data streams or data structures from any ofa variety of sources, as discussed above. Utilities 120, 140 and 160,topology agent 170, analysis engine 180 and/or repository 190 may beimplemented purely in software or similar modules, and supported on anyof a variety of devices, such as on a mainframe or by a stand-alone ornetworked processor. Databases or other record structures may beincorporated as well. For example, repository 190 may be any suitablestorage device or space, including one or more tables, collections ofdata structures, databases, etc.

In a packet switched network, an approximation of queuing behavior(known as M/M/1 queuing) may be defined as:E[T]=1/(μ−λ).  (1)This means that the expected value of delay, E[T] (e.g., queuing andtransmission delay) of a packet is dependent upon a packet service rateμ and a packet arrival rate λ. See e.g., Alberto Leon-Garcia,Probability and Random Processes for Electrical Engineering, SecondEdition, Addison-Wesley, 1994; and Dimitri Bertsekas and RobertGallager, Data Networks, Second Edition, Prentice Hall, 1994; each ofwhich is hereby incorporated by reference in its entirety. Of course,the precise queuing behavior is very complex and dependent upon manyparameters. Such parameters include the exact size of the packets ofeach class of service, the exact service rate, the arrival rate of eachclass of service, etc. Depending on a desired implementation, acceptablecost and/or complexity, among other factors, such parameters and/orother may or may not be considered in implementing a planning tool.

In previous work by the Applicants, making the service rate μ correspondto link size and making the link usage (bps) correspond to arrival ratewas studied. George Elmasry and C. John McCann, “Bottleneck Discovery inLarge-Scale Networks Based on the Expected Value of Per-hop Delay,”Proceedings of Milcom 2003, Boston, Mass., Oct. 13-16, 2003, herebyincorporated by reference in its entirety. Among other observations, ithas been noted that:

-   -   Assuming that μ corresponds to the link size may be valid,        particularly in dealing with a wireless network having tight        bandwidth, where the service rate over a link is bounded by link        capacity.

Using aggregate bps as λ excludes dropped traffic (e.g., trafficshaping, etc., performed by routers or other equipment need not beconsidered).

The above M/M/1 based formula is per packet. The assumption ofconsidering it per bit is equivalent to assuming that all packets havethe same size.

Even when such assumptions and/or approximations are made, however, ithas been found that a fairly strong correlation exists between theactual measured network queuing delay and the estimated E[T], whereservice rate μ corresponds to the link size and link usage (bps)corresponds to arrival rate. In one embodiment, certain concepts inaccordance with the present invention exploit this correlation togenerate a robust analysis tool for capacity planning.

Equation (1) above can be expressed as follows: $\begin{matrix}{{E\left\lbrack T_{i} \right\rbrack} = {\frac{1}{\mu - \lambda} = {\frac{1}{\mu}*{\frac{1}{1 - {\lambda/\mu}}.}}}} & (2)\end{matrix}$Considering the right hand side of the equation, which has two parts${\frac{1}{\mu}\quad{and}\quad\frac{1}{1 - {\lambda/\mu}}},$the first part shows that as the link size μ increases, the term$\frac{1}{\mu}$decreases, which decreases the expected value of delay E[T]. In otherwords, one can expect that as the link size increases, the link islikely to cause less congestion. The second part states that as the linkpercent utilization λ/μ increases, the term$\frac{1}{1 - {\lambda/\mu}}$increases, which increases the expected value of delay E[T]. In otherwords, one can expect that as the link percent utilization increases,the link is likely to cause more congestion. Thus, one can expect linkcontribution to an occurred congestion to be defined by both the linksize and the link utilization. As mentioned above, the invention enablesa robust analysis by passing each link through four separate tests basedon the above formula.

Certain of these concepts are illustrated by way of example in FIG. 2,which shows a plot of the expected value of delay versus percentutilization using formula (1) for three sizes of links, i.e., 0.5 MHz,0.75 MHz and 1.0 MHz bandwidth (BW). By considering the horizontaldotted line that crosses the vertical axes at 5, one can predict that a0.5 MHz link utilized at 60% will experience a comparable delay to thatexperienced as a 1.0 MHz link utilized at 80%.

In one embodiment of the invention, 4 tests are applied to each linkbased on formula (1) and the above explanation. In one such embodiment,the four tests are:

1—Expected Value of Delay Thresholds

-   -   This test defines links where the expected value of delay        exceeds an upper bound threshold T_(U) (indicating        over-utilization), links where the expected value of delay is        less than a lower bound T_(L) (indicating underutilization), and        links in between (neither over-utilized nor underutilized). See        FIG. 3, described in greater detail below.

2—First Derivative

-   -   This test identifies links where the rate of change (slope) of        the curve shown in FIG. 2 is high, links where the slope of the        curve in FIG. 2 is low, or links with slopes in an intermediate        range.

3—Second Derivative

-   -   This test identifies links where the acceleration of expected        value of delay, shown in FIG. 2, is high, links where the        acceleration of the curve in FIG. 2 is low, or links where the        acceleration is within the proper range.

4—Standard Deviation

-   -   This test will analyze the expected value of delay per link in        relationship to all the other links in the network. The test is        used to determine whether the selected network topology deviates        from or gets closer to optimality. In other words, if the OTM        ad-hoc network (or other network of interest) generates a        topology such that high rate radios are assigned to high rate        links and low rate radios are assigned to low rate links, the        standard deviation of the expected value of delay between all        links will be low. On the other hand, if the network selected        topology such that low rate radios are assigned to high rate        links and high rate radios are assigned to low rate links, the        standard deviation of the expected value of delay between all        links will be comparatively high.        Test 1: Expected Value of Delay Threshold

One can anticipate that if the expected value of delay of a given linkexceeds a certain threshold, the link can be considered congested. Forexample, the link may be congested in the sense that any pair of nodescommunicating over it are likely to suffer from some level of QoSdegradation. The challenge is to find this threshold, which may dependon factors such as the nature of traffic. Certain issues to considermight include: whether the link carries mostly time-sensitive ornon-time-sensitive traffic; how the traffic will be shaped; what is thebehavior of the scheduler serving the queues, etc. One basis for theestimation is through the use of any of a variety of network modelingand/or simulation tools, such as those offered by OPNET® Technologies,Inc., Bethesda, Md.

The curve in FIG. 3 shows a plot of percent utilization versus linkcapacity, where the expected value of delay is upper-bounded by T_(U)=2microseconds per bit and lower-bounded by T_(L)=0.2 microseconds perbit. The area between the two curves defines the range of acceptableutilization of the link as a function of link capacity in thisembodiment of the invention. The area above the T_(U) curve reflectsover-utilized links and the area under the T_(L) curve reflectsunder-utilized links. In such an embodiment, it is observed that if alink size is small, the link can cause congestion with relatively lowutilization. If a link size is large, any fluctuation in the percentutilization of the link can move the link from the under-utilized areato the over-utilized area.

Test 2: First Derivative

As mentioned above, in one embodiment, this test addresses, in FIG. 2,links where the slope of the curve shown is high, links where the slopeof the curve is low, or links within the proper range of utilization.This test attempts to anticipate possible congestion from any slightincrease in rate of change of E[T] versus link utilization.

For example, if r is set to be equal to percent utilization, i.e.,r=100*λ/μ and d(r) represents the expected value of delay in microbitsper second, Equation (2) above becomes${{d(r)} = {\frac{10^{6}}{\mu - \lambda} = {\frac{10^{6}}{\mu}*\frac{1}{1 - {\lambda/\mu}}}}},$and substituting in r becomesd(r)=10⁶/μ(1−r/100).Factoring out 1/100 from the denominator yieldsd(r)=10⁸/μ(100−r),with the first derivative being represented byd ¹(r)=10⁸/μ(100−r)².  (3)Equation (3) can be used to map each link to a range of the firstderivative based on link capacity and percent utilization.Test 3: Second Derivative

The concavity of the curve in FIG. 2 obtained through the secondderivative adds information to that obtained from the first derivative.Starting from Equation (3), the second derivative can be obtained asd ¹¹(r)=2*10⁸/[μ(100−r)³].  (4)Equation (4) can be used to map each link to a range of secondderivatives based on link capacity and percent utilization.Test 4: Standard Deviation

As mentioned above, in one embodiment, this test analyzes the expectedvalue of delay per link relative to all the other links in the network.

The sum of expected values of delay of all links can be obtained asfollows: $\begin{matrix}{{{E\left\lbrack T_{sum} \right\rbrack} = {\sum\limits_{i = 1}^{n}{E\left\lbrack T_{i} \right\rbrack}}},} & (5)\end{matrix}$where n is the total number of links in the network under analysis.

Based on Equations (2) and (5), a cost function C_(i) for a link i canbe obtained as follows: $\begin{matrix}{C_{i} = {\frac{E\left\lbrack T_{i} \right\rbrack}{E\left\lbrack T_{sum} \right\rbrack}*100.}} & (6)\end{matrix}$C_(i) then reflects the contribution of link i to the overall expectedvalue of delay.

The above analysis can be further appreciated by first focusing on thelink with the highest cost function, in terms of contribution to delay.It is first assumed that the n cost functions of all n links arescanned, and that C_(j) has the highest value; that is, C_(j) is thehighest in {C₁, C₂, . . . , C_(i), . . . C_(n)}. This also implies thatC_(j) is higher than the average cost, i.e.,C _(j) *n≧100.  (7)

If the OTM ad-hoc network or other pertinent network is given enoughresources (spectrum, UAVs, radio interfaces per node, relays, etc.) andthe network topology is selected properly (e.g., high capacityfrequencies are given to high rate links, low capacity frequencies aregiven to low rate links, the number of links for high rate communicatingpairs is minimized, etc.), the standard deviation between the costfunction of all the links should be desirably minimized. This can beindicated in Equation (7) by making C_(j)*n a minimal, and potentiallypredetermined, percentage above 100.

On the other hand, if the OTM ad-hoc network is not given enoughresources, or the network topology is not selected properly, thestandard deviation between the cost functions of all the links isexpected to be higher. This can be indicated in Equation (5) by makingC_(j)*n a comparably greater percentage above 100. This can be expressedas follows:C _(j) *n≧100+Δ,  (8)where Δ>0, and Δ increases as the topology deviates from optimality anddecreases as the topology approaches optimality.

In one embodiment, a discriminating value for Δ is determined asfollows. It is assumed that the expected values of delay are normallydistributed. To mitigate the impact of large outlier values, the medianmay be a more robust measure of central tendency than the mean. Sortingthe E[T_(i)] by value, letting m be the median value of the E[T_(i)],and letting M be the n/3 value above the median, it can be observed thatapproximately 2/3 of the values will be within plus or minus onestandard deviation of the mean. Thus, M—m would be approximately s,where s is an estimate of the standard deviation of the expected valuesof delay. Consequently, Δ is just s translated into cost terms, i.e., interms of a contribution to delay: $\begin{matrix}{\Delta = {\frac{s*100*n}{E\left\lbrack T_{ete} \right\rbrack}.}} & (9)\end{matrix}$

This test can be used as follows:

-   -   Rank all links to a group of links with cost functions exceeding        the mean plus twice the standard deviation, a group of links        with cost functions less than the mean minus twice the standard        deviation, and a group of links with cost functions within the        twice standard deviation.

The value of Δ can be used to indicate how the topology formation of theOTM ad-hoc network converges or diverges from optimality.

To illustrate further, an example is now provided. The following tablesshow exemplary threshold values and corresponding test scores that maybe established in an embodiment of the invention for the tests describedabove: Expected Value: Score Lower Threshold Upper Threshold 1 0.00.0000002 2 0.0000002 0.000002 3 0.000002 0.000003 4 0.000003 0.000004 50.000004 0.0005 6 Lambda >= mu (λ >= μ)

First Derivative: Score Lower Threshold Upper Threshold 1 0.0 1.0 2 1.028.6 3 28.6 57.3 4 57.3 95.5 5 95.5 573.0 6 573.0 1146.0

Second Derivative: Score Lower Threshold Upper Threshold 1 0.0 0.0005 20.0005 0.02 3 0.02 0.03 4 0.03 0.1 5 0.1 0.5 6 Lambda >= mu (λ >= μ)

Test Scores: As shown, for each of the four tests, lower and upperthresholds are established for 5 gradations of values, while a 6^(th)state is reserved for an impossible request. In one embodiment, eachstage is assigned a numerical value, indicating a health score, havingone of the following meanings:

-   -   1: very underutilized    -   2: underutilized    -   3: good    -   4: slightly over utilized    -   5: highly over utilized    -   6: impossible

In an implementation in which four tests are implemented, each yields anumerical value and the state of a link is assigned a final score. Inone embodiment, the final score is an average of the scores for the fourtests, although other possibilities are contemplated. The final scorereflects the link overall health and may be used to produce reports forthe planner, color code the link in a GUI showing the planned networkstate, among other uses.

Yet additional examples are now provided that include observed numericaldata for different link sizes and different link utilization. The scoreof each test and the corresponding overall link health is shown. Thesevalues were obtained from log files used with a capacity analysis toolbuilt using the above algorithm in accordance with the presentinvention.

EXAMPLE 1 Planned Rate is Higher than Planned Link BW

Effective BW (mu): 6109337.279102559 Data Rate (lambda):6244983.906602561 SEVERE CONGESTION Overall Health 6

EXAMPLE 2 Slightly Underutilized Small Link

Effective BW (mu): 1250000.0 Data Rate (lambda): 346901.51 E[T]1.107298939233084E−6 Delay Rating 2 Delay trend Rating 1 DelayAcceleration 3 Two Sigma 3 Overall Health 2

EXAMPLE 3 Over-Utilized Medium Size Link

Effective BW (mu) 4547398.458333332 Data Rate (lambda) 4394263.991666664E[T] 6.5302085269721105E−6 Delay Rating 5.0 Delay trend Rating 5 DelayAcceleration 5 Two Sigma 5 Overall Health 5

EXAMPLE 4 Well-Balanced Small Link

Effective BW (mu): 1250000.0 Data Rate (lambda): 433072.897115 E[T]1.2240994287849479E−6 Delay Rating 2.0 Delay trend Rating 1 DelayAcceleration 3 Two Sigma 5 Overall Health 3

EXAMPLE 5 Underutilized Large Link

Effective BW (mu): 1.07E7 Data Rate (lambda): 8703322.215 E[T]5.008319357596409E−7 Delay Rating 2.0 Delay trend Rating 1 DelayAcceleration 3 Two Sigma 3 Overall Health 2

EXAMPLE 6 Slightly Over-Utilized Large Link

Effective BW (mu): 1.08E7 Data Rate (lambda): 9448544.45320513 E[T]7.399429469741811E−7 Delay Rating 2.0 Delay trend Rating 5 DelayAcceleration 4 Two Sigma 3 Overall Health 4

EXAMPLE 7 Over-Utilized Very Large Link

Effective BW (mu): 1.5018528125000006E7 Data Rate (lambda):1.4518529011666672E7 E[T] 2.000003546672956E−6 Delay Rating 3.0 Delaytrend Rating 5 Delay Acceleration 5 Two Sigma 3 Overall Health 4

From a review of these examples, one skilled in the art will appreciatethat the results achieved from a practice of the present invention couldbe used for many purposes. For example, as discussed above, links on auser interface of a network planner can be color-coded according to anoverall health score, in accordance with military operational standards,among other network planning possibilities.

Network health scores can be determined in a number of ways. Forexample, as discussed above, the analysis agent 180 may calculate anoverall network health by averaging results determined by the analysisagent 180 for one or more links at a certain time, or over a period oftime. In one embodiment, such an average is weighted. Of course,weighting may be based on a variety of factors and/or carried out in aplurality of ways. In one embodiment, the average is weighted based on aprecedence of traffic associated with one or more links. Such precedencemay be based on a timing of a message, an indicated urgency, sender,receiver, etc., as discussed in part above. The average may also beweighted based on a total number of links having one or morepredetermined health scores and/or an amount of traffic. For example, anumber of links having a score of 4, 5, or 6 may be determined to be akey characteristic of a particular network, and therefore relied upon.Similarly, an amount of traffic over links having a health score of 2, 3or 4 may be sought to be maximized in a certain implementation. In thatcase, a system may consider only whether a score is 1) a 2, 3, or 4, or2) is not a 2, 3, or 4, while disregarding any distinction within theclass of scores 2, 3 and 4. In one embodiment, an amount of trafficusing more than one (or another predetermined number) of links may beconsidered. One skilled in the art will appreciate that countless otherpossibilities exist as well, depending on a desired result, simplicity,etc.

It should be noted that, as discussed above, the methods disclosedherein in accordance with the invention need not include all disclosedsteps or necessarily be practiced in a described order. For example, acapacity tool need not rely upon any or all of the four tests disclosedabove, as one skilled in the art will readily appreciate that obviousvariations may be made depending on a desired implementation. Similarly,disclosed systems are by way of example only and are therefore subjectto much potential variation. In addition, it is contemplated that methodsteps and/or system elements disclosed in one example or embodiment maybe combined with one or more other steps and/or elements in one or moreother examples or embodiments, to achieve a system and/or method inaccordance with the invention. For these and other reasons, theinventions disclosed should not be limited to embodiments presentedherein, but rather are defined more generally, as by the appendedclaims.

1. A system for capacity analysis of a link among a plurality of linksin an ad hoc wireless network, the system comprising: a traffic lay-downutility configured to determine the traffic demand over the link amongthe plurality of links defining a wireless network at a time t₁; atopology agent, the topology agent being configured to output a networktopology at a time t₁; and an analysis agent, the analysis agent beingconfigured to: calculate an amount of accumulated traffic over the link;calculate a link capacity for the link; determine a delay threshold ofthe link; determine a rate of change of an expected value of a delaywith respect to a link utilization of the link; determine anacceleration of the expected value of a delay with respect to a linkutilization of the link; and determine whether the link is optimal bycomparing the link against the remaining links of the plurality oflinks; whereby the analysis agent is configured to determine the healthof the link at time t₁ based on the amount of accumulated traffic overthe link, the link capacity, the delay threshold, the rate of change ofan expected value of a delay with respect to a link utilization, therate of change of the rate of change of the expected value of the delaywith respect to the link utilization, and the comparison of the linkagainst the remaining links of the plurality of links.
 2. The system ofclaim 1, wherein the analysis agent is further configured to calculatean overall network health based on results determined by the analysisagent for the link.
 3. The system of claim 2, wherein the analysis agentcalculates the overall network health by averaging the resultsdetermined by the analysis agent for the link and for the remaininglinks of the plurality of links.
 4. The system of claim 2, furthercomprising: the analysis agent assigning a health score to the link andeach of the plurality of remaining links of the plurality of links basedon the determined health of the link at time t₁; wherein the analysisagent calculates the overall network health by averaging the healthscores for the link and for the remaining links of the plurality oflinks.
 5. The system of claim 4, further comprising: wherein theanalysis agent calculates the overall network health based on a weightedaverage of the health scores for the link and for the remaining links ofthe plurality of links.
 6. The system of claim 5, wherein the weightedaverage is weighted based on a precedence of traffic associated with thelink and for the remaining links of the plurality of links.
 7. Thesystem of claim 5, wherein the weighted average is weighted based on atleast on of 1) a first total number of links having one or morepredetermined health scores, 2) an amount of traffic over a second totalnumber of links having one or more predetermined health scores, and 3)an amount of traffic using more than one of the link and the remaininglinks of the plurality of links.
 8. The system of claim 1, wherein theanalysis agent comprises: means for calculating an expected value ofdelay E[T] of the link; means for calculating a first derivative of thedelay E[T] of the link; means for calculating a second derivative of thedelay E[T] of the link; and means for determining whether the link isoptimal by comparing the link against the remaining links of theplurality of links.
 9. The system of claim 8, wherein E[T] is defined asE[T]=1/(μ−λ), μ being a packet service rate and λ being a packet arrivalrate.
 10. A method for determining network health for a link in acommunication network, comprising: receiving a traffic demand record ata time t₁; receiving a given topology at time t₁; determining the pathover which traffic will be routed; calculating accumulated traffic forthe link in the communication network; calculating the maximum capacityof the link; and providing network results for time t₁ based on saidreceiving, determining and calculating steps.
 11. The method of claim10, wherein the traffic demand record at time t₁ is received from atraffic lay down utility.
 12. The method of claim 10, wherein the giventopology is received from a topology agent.
 13. The method of claim 10,wherein the path over which traffic will be routed is determined basedon a methodology of an applicable router.
 14. The method of claim 13,wherein the methodology of the applicable router is shortestcommunication path.
 15. The method of claim 10, further comprising:repeating said receiving the traffic demand record at time t₁ whenadditional traffic demand records exist for time t_(l).
 16. The methodof claim 10, wherein the network results for time t₁ are provided to aplanned network information repository.
 17. The method of claim 16,wherein the network results include at least one of a link size, a linkutilization and a link health.
 18. The method of claim 10, furthercomprising: reporting the network results to a topology agent; and basedon the network results reported, modifying the given topology to achievea modified topology.
 19. The method of claim 10, wherein the method ispracticed for a predetermined plurality of times t₁ to t_(n) thattogether comprise a planned scenario, the method further comprising:when aggregate network results for all times t₁ to t_(n) have beenreceived, analyzing the aggregate network results for all times t₁ tot_(n); and generating a report for the planned scenario.
 20. The methodof claim 19, wherein said generating a report for the planned scenariois performed by an analysis agent; and wherein the report is provided toa planned network information repository.